Search results for " Uncertainty"

Multivariate nonparametric tests in a randomized complete block design

2003

AbstractIn this paper multivariate extensions of the Friedman and Page tests for the comparison of several treatments are introduced. Related unadjusted and adjusted treatment effect estimates for the multivariate response variable are also found and their properties discussed. The test statistics and estimates are analogous to the traditional univariate methods. In test constructions, the univariate ranks are replaced by multivariate spatial ranks (J. Nonparam. Statist. 5 (1995) 201). Asymptotic theory is developed to provide approximations for the limiting distributions of the test statistics and estimates. Limiting efficiencies of the tests and treatment effect estimates are found in the…

Association between climate and new daily diagnoses of COVID-19

2020

AbstractBackgroundAlthough evidence is accumulating that climate conditions may positively or negatively influence the scale of coronavirus disease 2019 (COVID-19) outbreaks, uncertainty remains concerning the real impact of climate factors on viral transmission. Methods. The number of new daily cases of COVID-19 diagnosed in Verona (Italy) was retrieved from the official website of Veneto Region, while information on daily weather parameters in the same area was downloaded from IlMeteo website, a renowned Italian technological company specialized in weather forecasts. The search period ranged between March 1 to November 11, 2020. The number of new daily COVID-19 cases and meteorological da…

Probabilistic Flood Hazard Mapping Using Bivariate Analysis Based on Copulas

2017

This study presents a methodology to extract probabilistic flood hazard maps in an area subject to flood risk, taking into account uncertainties in the definition of design hydrographs. Particularly, the authors present a new method to produce probabilistic inundation and flood hazard maps in which the hydrological input (i.e., synthetic flood design event) to a 2D hydraulic model has been obtained by using a bivariate statistical analysis (copulas) to generate flood peak discharges and volumes. This study also aims to quantify the contribution of boundary conditions’ uncertainty in order to evaluate the effect of this uncertainty source on probabilistic flood hazard mapping. Different comb…

Characters of relative p'-degree over normal subgroups

2013

Let Z be a normal subgroup of a finite group G , let ??Irr(Z) be an irreducible complex character of Z , and let p be a prime number. If p does not divide the integers ?(1)/?(1) for all ??Irr(G) lying over ? , then we prove that the Sylow p -subgroups of G/Z are abelian. This theorem, which generalizes the Gluck-Wolf Theorem to arbitrary finite groups, is one of the principal obstacles to proving the celebrated Brauer Height Zero Conjecture

I comportamenti virtuosi tra rispetto delle norme e aspettative sociali

2020

Il capitolo si sofferma sull’azione rispetto alle norme spiegando come le aspettative sociali ricoprano un ruolo cruciale nella gestione delle informazioni utili per muoverci nell’ambiente in cui viviamo, giacchè ci consentono di ridurre l’incertezza derivante dall’assunzione di determinate scelte, di stimare quali possibili effetti avrà una nostra azione in un determinato tempo e in uno specifico contesto, o ancora di valutare quali possono essere le strategie che possiamo mettere in campo per ridurre la probabilità di una risposta indesiderata da parte degli altri. The chapter focuses on action with respect to the rules, explaining how social expectations play a crucial role in the man…

Limits on entropic uncertainty relations

2010

We consider entropic uncertainty relations for outcomes of the measurements of a quantum state in 3 or more mutually unbiased bases (MUBs), chosen from the standard construction of MUBs in prime dimension. We show that, for any choice of 3 MUBs and at least one choice of a larger number of MUBs, the best possible entropic uncertainty relation can be only marginally better than the one that trivially follows from the relation by Maassen and Uffink for 2 bases.

Corrigendum to “Registration of surfaces minimizing error propagation for a one-shot multi-slit hand-held scanner” [Pattern Recognition 41 (6) 2055–2…

2009

On the Reliability of Optimization Results for Trigeneration Systems in Buildings, in the Presence of Price Uncertainties and Erroneous Load Estimati…

2016

Cogeneration and trigeneration plants are widely recognized as promising technologies for increasing energy efficiency in buildings. However, their overall potential is scarcely exploited, due to the difficulties in achieving economic viability and the risk of investment related to uncertainties in future energy loads and prices. Several stochastic optimization models have been proposed in the literature to account for uncertainties, but these instruments share in a common reliance on user-defined probability functions for each stochastic parameter. Being such functions hard to predict, in this paper an analysis of the influence of erroneous estimation of the uncertain energy loads and pric…

Respondent Uncertainty and Ordering Effect on Willingness to Pay for Salt Marsh Conservation in the Brest Roadstead (France)

2017

International audience; This paper explores the potential link between the sensitivity of willingness to pay (WTP) to the order of presenting bid amounts in contingent valuation questions (ordering effect) and respondent uncertainty. The resource being valued is a public project to protect salt marshes against the spread of an invasive aquatic plant in the Brest roadstead (France). Valuation uncertainty is captured through a variant of payment card format where respondents are given the opportunity to report their WTP as either a single value (Option A) or an interval of values (Option B). The ordering effect is tested using both parametric models that ignore and control for the potential s…

Comparing Boosting and Bagging for Decision Trees of Rankings

2021

AbstractDecision tree learning is among the most popular and most traditional families of machine learning algorithms. While these techniques excel in being quite intuitive and interpretable, they also suffer from instability: small perturbations in the training data may result in big changes in the predictions. The so-called ensemble methods combine the output of multiple trees, which makes the decision more reliable and stable. They have been primarily applied to numeric prediction problems and to classification tasks. In the last years, some attempts to extend the ensemble methods to ordinal data can be found in the literature, but no concrete methodology has been provided for preference…